For the past two years, the AI boom has largely been framed as a GPU story. Demand for high-performance graphics chips has surged, cloud providers have scrambled for supply and AI infrastructure has become synonymous with GPU clusters.
However, Google has recently announced that it’s expanded its partnership with Intel, suggesting the AI hardware race may be broader than that.
According to Reuters, Google and Intel are deepening their collaboration around AI-optimised data centre chips, with Google planning to deploy Intel’s latest Xeon processors across parts of its infrastructure. Experts seem to agree that this move signals that CPUs – that up until now, have been seen as secondary in the AI boom – may still play a critical role as models move from training to deployment.
The AI Bottleneck Isn’t Just Compute Anymore
Much of the AI infrastructure conversation has focused on training massive models. GPUs dominate this issue, because they handle parallel workloads efficiently. But inference, which is actually running AI models at scale, is a different challenge entirely, and one that’s now in the spotlight.
According to reporting from CNBC, Google is working more closely with Intel to optimise chips for AI workloads, particularly those tied to inference and enterprise deployments. This matters because inference workloads tend to prioritise efficiency, cost and scalability rather than raw compute power.
In other words, the next phase of AI may be less about building the biggest models and more about running them cheaply and reliably. And that means, there may be a top dog.
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A Shift Toward Diversified AI Infrastructure
Google’s move also reflects a broader industry trend – that is, companies are diversifying their AI hardware stacks. Relying entirely on GPUs is expensive, supply-constrained and difficult to scale globally.
By expanding its partnership with Intel, Google seems to be hedging against those constraints. CPUs may not replace GPUs for training frontier models, but they definitely could play a bigger role in handling everyday AI workloads, enterprise tools and on-demand services.
It seems as though the collaboration is aimed at improving performance for AI-heavy applications while also lowering infrastructure costs, and given the fact that most of the world is worrying about an AI bubble at the moment, this is a key consideration as AI moves from experimentation to production.
Why This Matters for Startups
For startups, the implications could be significant because in theory, if AI infrastructure becomes less GPU-dependent, it may lower barriers to entry. Access to AI capabilities could expand beyond companies that can afford large GPU clusters, meaning that smaller companies with lower budgets may finally get a seat at the table.
More efficient CPU-based inference could also make edge deployments more practical, opening opportunities for AI applications in mobile, enterprise software and real-time analytics.
In that sense, Google’s move isn’t just about hardware. It’s actually about how accessible AI becomes, even if that’s not necessarily the company’s intention.
The AI Chip Wars Are Evolving
This partnership also highlights how the AI chip race is evolving beyond a single architecture. That is, GPUs still dominate training, but CPUs, custom accelerators and specialised AI chips are increasingly part of the conversation – in fact, they’re changing the conversation entirely.
As AI workloads mature, the focus is shifting from brute-force compute to efficiency. That’s where Intel’s strength in data centre CPUs becomes relevant again.
Google’s decision to deepen ties suggests the next phase of AI infrastructure may be more balanced – mixing GPUs for training with CPUs and other chips for deployment.
It’s Not the End of GPUs, But It’s Not the Whole Story Either
GPUs aren’t going anywhere. They’re still essential for training large-scale models and pushing the frontier of AI capability; there’s simply no way around it. But at the same time, Google’s expanded collaboration with Intel suggests that the industry is beginning to rethink whether GPUs alone can carry the next wave of AI growth.
If inference demand continues to grow faster than training, CPUs and alternative architectures could become more important. That could reshape cloud economics, AI accessibility and even startup competition.
For now, Google’s move looks like a strategic bet, but it also raises a bigger question – as AI scales globally, will efficiency matter more than raw compute power? If so, the AI hardware race may be entering a new phase, and it’s no longer just about GPUs.
